429 lines
16 KiB
Python
429 lines
16 KiB
Python
"""
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模型列表获取API路由
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提供从各个 AI 厂商 API 获取可用模型列表的代理接口
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"""
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import os
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from typing import Dict, List, Optional
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import httpx
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import tomlkit
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from fastapi import APIRouter, Depends, HTTPException, Query
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from src.common.logger import get_logger
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from src.config.config import CONFIG_DIR
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from src.config.model_configs import APIProvider
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from src.llm_models.openai_compat import build_openai_compatible_client_config, normalize_openai_base_url
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from src.webui.dependencies import require_auth
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from src.webui.utils.network_security import validate_public_url
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logger = get_logger("webui")
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router = APIRouter(prefix="/models", tags=["models"], dependencies=[Depends(require_auth)])
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# 模型获取器配置
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MODEL_FETCHER_CONFIG = {
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# OpenAI 兼容格式的提供商
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"openai": {
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"endpoint": "/models",
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"parser": "openai",
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},
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# Gemini 格式
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"gemini": {
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"endpoint": "/models",
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"parser": "gemini",
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},
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}
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def _normalize_url(url: str) -> str:
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"""规范化 URL（去掉尾部斜杠）。"""
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return normalize_openai_base_url(url) if url else ""
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def _parse_openai_response(data: Dict) -> List[Dict]:
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"""
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解析 OpenAI 格式的模型列表响应
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格式: { "data": [{ "id": "gpt-4", "object": "model", ... }] }
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"""
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if "data" not in data or not isinstance(data["data"], list):
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return []
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return [
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{
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"id": model["id"],
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"name": model.get("name") or model["id"],
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"owned_by": model.get("owned_by", ""),
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}
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for model in data["data"]
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if isinstance(model, dict) and "id" in model
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]
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def _parse_gemini_response(data: Dict) -> List[Dict]:
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"""
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解析 Gemini 格式的模型列表响应
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格式: { "models": [{ "name": "models/gemini-pro", "displayName": "Gemini Pro", ... }] }
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"""
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models = []
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if "models" in data and isinstance(data["models"], list):
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for model in data["models"]:
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if isinstance(model, dict) and "name" in model:
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# Gemini 的 name 格式是 "models/gemini-pro"，我们只取后面部分
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model_id = model["name"]
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if model_id.startswith("models/"):
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model_id = model_id[7:] # 去掉 "models/" 前缀
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models.append(
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{
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"id": model_id,
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"name": model.get("displayName") or model_id,
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"owned_by": "google",
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}
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)
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return models
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async def _fetch_models_from_provider(
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base_url: str,
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api_key: str,
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endpoint: str,
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parser: str,
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client_type: str = "openai",
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auth_type: str = "bearer",
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auth_header_name: str = "Authorization",
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auth_header_prefix: str = "Bearer",
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auth_query_name: str = "api_key",
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default_headers: Optional[Dict[str, str]] = None,
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default_query: Optional[Dict[str, str]] = None,
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) -> List[Dict]:
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"""从提供商 API 获取模型列表。
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Args:
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base_url: 提供商的基础 URL。
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api_key: API 密钥。
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endpoint: 获取模型列表的端点。
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parser: 响应解析器类型。
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client_type: 客户端类型。
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auth_type: OpenAI 兼容接口的鉴权方式。
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auth_header_name: Header 鉴权时使用的请求头名称。
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auth_header_prefix: Header 鉴权时使用的请求头前缀。
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auth_query_name: Query 鉴权时使用的查询参数名称。
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default_headers: 默认附带的请求头。
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default_query: 默认附带的查询参数。
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Returns:
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List[Dict]: 解析后的模型列表。
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"""
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try:
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base_url = validate_public_url(_normalize_url(base_url))
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except ValueError as e:
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raise HTTPException(status_code=400, detail=str(e)) from e
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url = f"{base_url}{endpoint}"
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# 根据客户端类型设置请求头
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headers = {}
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params = {}
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if client_type == "gemini":
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# Gemini 使用 URL 参数传递 API Key
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params["key"] = api_key
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else:
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provider = APIProvider(
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name="webui-openai-compatible-fetcher",
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base_url=base_url,
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api_key=api_key,
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client_type="openai",
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auth_type=auth_type,
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auth_header_name=auth_header_name,
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auth_header_prefix=auth_header_prefix,
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auth_query_name=auth_query_name,
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default_headers=default_headers or {},
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default_query=default_query or {},
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)
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client_config = build_openai_compatible_client_config(provider)
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headers.update(client_config.default_headers)
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params.update(client_config.default_query)
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# build_openai_compatible_client_config 在“默认 Bearer”场景下，
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# 会把 api_key 留在 client_config.api_key 中交给 OpenAI SDK 自行注入 Authorization 头，
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# 而不会写入 default_headers。这里我们用 httpx 直接发请求，需要手动补上鉴权头/参数。
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if client_config.api_key and "Authorization" not in headers:
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headers["Authorization"] = f"Bearer {client_config.api_key}"
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try:
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async with httpx.AsyncClient(timeout=30.0) as client:
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response = await client.get(url, headers=headers, params=params)
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response.raise_for_status()
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data = response.json()
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except httpx.TimeoutException as e:
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raise HTTPException(status_code=504, detail="请求超时，请稍后重试") from e
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except httpx.HTTPStatusError as e:
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# 注意：使用 502 Bad Gateway 而不是原始的 401/403，
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# 因为前端的 fetchWithAuth 会把 401 当作 WebUI 认证失败处理
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if e.response.status_code == 401:
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raise HTTPException(status_code=502, detail="API Key 无效或已过期") from e
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elif e.response.status_code == 403:
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raise HTTPException(status_code=502, detail="没有权限访问模型列表，请检查 API Key 权限") from e
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elif e.response.status_code == 404:
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raise HTTPException(status_code=502, detail="该提供商不支持获取模型列表") from e
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else:
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raise HTTPException(
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status_code=502, detail=f"上游服务请求失败 ({e.response.status_code}): {e.response.text[:200]}"
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) from e
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except Exception as e:
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logger.error(f"获取模型列表失败: {e}")
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raise HTTPException(status_code=500, detail=f"获取模型列表失败: {str(e)}") from e
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# 根据解析器类型解析响应
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if parser == "openai":
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return _parse_openai_response(data)
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elif parser == "gemini":
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return _parse_gemini_response(data)
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else:
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raise HTTPException(status_code=400, detail=f"不支持的解析器类型: {parser}")
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def _get_provider_config(provider_name: str) -> Optional[Dict]:
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"""
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从 model_config.toml 获取指定提供商的配置
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Args:
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provider_name: 提供商名称
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Returns:
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提供商配置，如果未找到则返回 None
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"""
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config_path = os.path.join(CONFIG_DIR, "model_config.toml")
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if not os.path.exists(config_path):
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return None
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try:
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with open(config_path, "r", encoding="utf-8") as f:
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config_data = tomlkit.load(f)
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providers = config_data.get("api_providers", [])
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provider = next((provider for provider in providers if provider.get("name") == provider_name), None)
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return dict(provider) if provider is not None else None
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except Exception as e:
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logger.error(f"读取提供商配置失败: {e}")
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return None
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@router.get("/list")
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async def get_provider_models(
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provider_name: str = Query(..., description="提供商名称"),
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parser: str = Query("openai", description="响应解析器类型 (openai | gemini)"),
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endpoint: str = Query("/models", description="获取模型列表的端点"),
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):
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"""获取指定提供商的可用模型列表。
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通过提供商名称查找配置，然后请求对应的模型列表端点。
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"""
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# 获取提供商配置
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provider_config = _get_provider_config(provider_name)
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if not provider_config:
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raise HTTPException(status_code=404, detail=f"未找到提供商: {provider_name}")
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base_url = provider_config.get("base_url")
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api_key = provider_config.get("api_key")
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client_type = provider_config.get("client_type", "openai")
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if not base_url:
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raise HTTPException(status_code=400, detail="提供商配置缺少 base_url")
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if not api_key:
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raise HTTPException(status_code=400, detail="提供商配置缺少 api_key")
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resolved_endpoint = provider_config.get("model_list_endpoint", endpoint) if endpoint == "/models" else endpoint
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# 获取模型列表
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models = await _fetch_models_from_provider(
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base_url=base_url,
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api_key=api_key,
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endpoint=resolved_endpoint,
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parser=parser,
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client_type=client_type,
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auth_type=provider_config.get("auth_type", "bearer"),
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auth_header_name=provider_config.get("auth_header_name", "Authorization"),
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auth_header_prefix=provider_config.get("auth_header_prefix", "Bearer"),
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auth_query_name=provider_config.get("auth_query_name", "api_key"),
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default_headers=provider_config.get("default_headers", {}),
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default_query=provider_config.get("default_query", {}),
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)
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return {
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"success": True,
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"models": models,
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"provider": provider_name,
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"count": len(models),
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}
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@router.get("/list-by-url")
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async def get_models_by_url(
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base_url: str = Query(..., description="提供商的基础 URL"),
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api_key: str = Query(..., description="API Key"),
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parser: str = Query("openai", description="响应解析器类型 (openai | gemini)"),
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endpoint: str = Query("/models", description="获取模型列表的端点"),
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client_type: str = Query("openai", description="客户端类型 (openai | gemini)"),
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auth_type: str = Query("bearer", description="鉴权方式 (bearer | header | query | none)"),
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auth_header_name: str = Query("Authorization", description="Header 鉴权名称"),
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auth_header_prefix: str = Query("Bearer", description="Header 鉴权前缀"),
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auth_query_name: str = Query("api_key", description="Query 鉴权参数名"),
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):
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"""通过 URL 直接获取模型列表。"""
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models = await _fetch_models_from_provider(
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base_url=base_url,
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api_key=api_key,
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endpoint=endpoint,
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parser=parser,
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client_type=client_type,
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auth_type=auth_type,
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auth_header_name=auth_header_name,
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auth_header_prefix=auth_header_prefix,
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auth_query_name=auth_query_name,
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)
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return {
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"success": True,
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"models": models,
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"count": len(models),
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}
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@router.get("/test-connection")
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async def test_provider_connection(
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base_url: str = Query(..., description="提供商的基础 URL"),
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api_key: Optional[str] = Query(None, description="API Key（可选，用于验证 Key 有效性）"),
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client_type: str = Query("openai", description="客户端类型 (openai | gemini)"),
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):
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"""
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测试提供商连接状态
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分两步测试：
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1. 网络连通性测试：向 base_url 发送请求，检查是否能连接
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2. API Key 验证（可选）：如果提供了 api_key，尝试获取模型列表验证 Key 是否有效
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返回：
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- network_ok: 网络是否连通
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- api_key_valid: API Key 是否有效（仅在提供 api_key 时返回）
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- latency_ms: 响应延迟（毫秒）
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- error: 错误信息（如果有）
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"""
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import time
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base_url = _normalize_url(base_url)
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if not base_url:
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raise HTTPException(status_code=400, detail="base_url 不能为空")
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try:
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base_url = validate_public_url(base_url)
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except ValueError as e:
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raise HTTPException(status_code=400, detail=str(e)) from e
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result = {
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"network_ok": False,
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"api_key_valid": None,
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"latency_ms": None,
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"error": None,
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"http_status": None,
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}
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# 第一步：测试网络连通性
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try:
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start_time = time.time()
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async with httpx.AsyncClient(timeout=10.0, follow_redirects=True) as client:
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# 尝试 GET 请求 base_url（不需要 API Key）
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response = await client.get(base_url)
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latency = (time.time() - start_time) * 1000
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result["network_ok"] = True
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result["latency_ms"] = round(latency, 2)
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result["http_status"] = response.status_code
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except httpx.ConnectError as e:
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result["error"] = f"连接失败：无法连接到服务器 ({str(e)})"
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return result
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except httpx.TimeoutException:
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result["error"] = "连接超时：服务器响应时间过长"
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return result
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except httpx.RequestError as e:
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result["error"] = f"请求错误：{str(e)}"
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return result
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except Exception as e:
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result["error"] = f"未知错误：{str(e)}"
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return result
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# 第二步：如果提供了 API Key，验证其有效性
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if api_key:
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try:
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start_time = time.time()
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async with httpx.AsyncClient(timeout=15.0, follow_redirects=True) as client:
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headers = {"Content-Type": "application/json"}
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params = {}
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if client_type == "gemini":
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# Gemini 使用 URL 参数传递 API Key
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params["key"] = api_key
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else:
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# OpenAI 兼容格式使用 Authorization 头
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headers["Authorization"] = f"Bearer {api_key}"
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# 尝试获取模型列表
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models_url = f"{base_url}/models"
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response = await client.get(models_url, headers=headers, params=params)
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if response.status_code == 200:
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result["api_key_valid"] = True
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elif response.status_code in (401, 403):
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result["api_key_valid"] = False
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result["error"] = "API Key 无效或已过期"
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else:
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# 其他状态码，可能是端点不支持，但 Key 可能是有效的
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result["api_key_valid"] = None
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except Exception as e:
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# API Key 验证失败不影响网络连通性结果
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logger.warning(f"API Key 验证失败: {e}")
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result["api_key_valid"] = None
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return result
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@router.post("/test-connection-by-name")
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async def test_provider_connection_by_name(
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provider_name: str = Query(..., description="提供商名称"),
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):
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"""
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通过提供商名称测试连接（从配置文件读取信息）
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"""
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# 读取配置文件
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model_config_path = os.path.join(CONFIG_DIR, "model_config.toml")
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if not os.path.exists(model_config_path):
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raise HTTPException(status_code=404, detail="配置文件不存在")
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with open(model_config_path, "r", encoding="utf-8") as f:
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config = tomlkit.load(f)
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# 查找提供商
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providers = config.get("api_providers", [])
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provider = next((item for item in providers if item.get("name") == provider_name), None)
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if not provider:
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raise HTTPException(status_code=404, detail=f"未找到提供商: {provider_name}")
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base_url = provider.get("base_url", "")
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api_key = provider.get("api_key", "")
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client_type = provider.get("client_type", "openai")
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if not base_url:
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raise HTTPException(status_code=400, detail="提供商配置缺少 base_url")
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# 调用测试接口
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return await test_provider_connection(
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base_url=base_url,
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api_key=api_key if api_key else None,
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client_type=client_type,
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)
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